Brain Cancer

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks.

The aim of this study was to develop an automated segmentation approach for small gross tumor volume...

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to ...

IDH glioma radiogenomics in the era of deep learning.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and p...

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers.

BACKGROUND: Pathological diagnosis of glioma subtypes is essential for treatment planning and progno...

Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience.

Online health communities (OHC) provide various opportunities for patients with chronic or life-thre...

Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the...

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the numb...

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threa...

[A Case of Locally Advanced Rectal Cancer Treated by Robot Assisted Intersphincteric Resection after Neoadjuvant Chemotherapy].

We present a case of locally advanced rectal cancer(LARC)treated by robot assisted intersphincteric ...

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT)....

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

With the massive use of computers, the growth and explosion of data has greatly promoted the develop...

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiot...

[Automation of Damage Detection and Damage Area Measurement of X-ray Protective Clothing Using Deep Learning].

PURPOSE: Damage to shielding sheets on X-ray protective clothing may be a cause of increased radiati...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

The causal role of somatic mutation and its interrelationship with gene expression profile during tu...

Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.

Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Auto...

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine.

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and...

Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.

Using medical images recorded in clinical practice has the potential to be a game-changer in the app...

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images.

Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the dis...

Machine learning for radiation outcome modeling and prediction.

AIMS: This review paper intends to summarize the application of machine learning to radiotherapy out...

Introduction to machine and deep learning for medical physicists.

Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep l...